隐式内容的自然语言分解提升文本表示质量
Natural Language Decompositions of Implicit Content Enable Better Text Representations
AI总结：
本文提出利用大语言模型将文本隐含内容显式化为命题集合，经人工验证后用于提升论证相似性评估、观点数据分析及立法行为建模等任务，证明建模语言背后意义对NLP及社会科学应用具有重要价值。
摘要：
人们在解读文本时，依赖的推理往往超越了所观察到的语言本身。受此观察启发，我们提出了一种文本分析方法，将隐含传达的内容显式地纳入考量。我们使用大型语言模型生成与已观察文本具有推理关联的命题集合，然后通过人工判断来验证所生成内容的合理性。将这些显式的隐式内容表示纳入多个涉及人类对话语解读的问题场景中，被证明是有用的：评估论证的相似性、理解大量观点数据以及建模立法行为。我们的结果表明，对观察到的语言背后的意义进行建模，而不仅仅是字面文本，是NLP的一个有价值的研究方向，尤其是其在社会科学中的应用。
英文摘要：
When people interpret text, they rely on inferences that go beyond the observed language itself. Inspired by this observation, we introduce a method for the analysis of text that takes implicitly communicated content explicitly into account. We use a large language model to produce sets of propositions that are inferentially related to the text that has been observed, then validate the plausibility of the generated content via human judgments. Incorporating these explicit representations of implicit content proves useful in multiple problem settings that involve the human interpretation of utterances: assessing the similarity of arguments, making sense of a body of opinion data, and modeling legislative behavior. Our results suggest that modeling the meanings behind observed language, rather than the literal text alone, is a valuable direction for NLP and particularly its applications to social science.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
